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Record W4285999454 · doi:10.1038/s41598-022-16607-3

Wildfire plumes in the Western US are reaching greater heights and injecting more aerosols aloft as wildfire activity intensifies

2022· article· en· W4285999454 on OpenAlexaboutno aff
Taylor Y. Wilmot, Derek V. Mallia, A. Gannet Hallar, John C. Lin

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersUniversity of UtahJet Propulsion LaboratoryNational Aeronautics and Space Administration
KeywordsEnvironmental scienceAtmospheric sciencesClimate changeClimatologyGeologyEcologyBiology

Abstract

fetched live from OpenAlex

By producing a first-of-its-kind, decadal-scale wildfire plume rise climatology in the Western U.S. and Canada, we identify trends toward enhanced plume top heights, aerosol loading aloft, and near-surface smoke injection throughout the American West. Positive and significant plume trends suggest a growing impact of Western US wildfires on air quality at the local to continental scales and support the notion that wildfires may have an increasing impact on regional climate. Overlap of identified trends with regions of increasing wildfire emissions and burn severity suggests a link to climate driven trends toward enhanced wildfire activity. Further, time series of plume activity point to a possible acceleration of trends over recent years, such that the future impacts to air quality and regional climate may exceed those suggested by a linear fit to the multi-decadal data. These findings have significant implications for human health and exacerbate concern for the climate-wildfire connection.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.225
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations55
Published2022
Admission routes1
Has abstractyes

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